Kernel density correlation based non-rigid point set matching for statistical model-based 2D/3D reconstruction

This paper presents a kernel density correlation based nonrigid point set matching method and shows its application in statistical model based 2D/3D reconstruction of a scaled, patient-specific model from an un-calibrated x-ray radiograph. In this method, both the reference point set and the floating point set are first represented using kernel density estimates. A correlation measure between these two kernel density estimates is then optimized to find a displacement field such that the floating point set is moved to the reference point set. Regularizations based on the overall deformation energy and the motion smoothness energy are used to constraint the displacement field for a robust point set matching. Incorporating this non-rigid point set matching method into a statistical model based 2D/3D reconstruction framework, we can reconstruct a scaled, patient-specific model from noisy edge points that are extracted directly from the x-ray radiograph by an edge detector. Our experiment conducted on datasets of two patients and six cadavers demonstrates a mean reconstruction error of 1.9 mm

ECM versus ICP for point registration

Iterative Closest Point (ICP) is a widely exploited method for point registration that is based on binary point-to-point assignments, whereas the Expectation Conditional Maximization (ECM) algorithm tries to solve the problem of point registration within the framework of maximum likelihood with point-to-cluster matching. In this paper, by fulfilling the implementation of both algorithms as well as conducting experiments in a scenario where dozens of model points must be registered with thousands of observation points on a pelvis model, we investigated and compared the performance (e.g. accuracy and robustness) of both ICP and ECM for point registration in cases without noise and with Gaussian white noise. The experiment results reveal that the ECM method is much less sensitive to initialization and is able to achieve more consistent estimations of the transformation parameters than the ICP algorithm, since the latter easily sinks into local minima and leads to quite different registration results with respect to different initializations. Both algorithms can reach the high registration accuracy at the same level, however, the ICP method usually requires an appropriate initialization to converge globally. In the presence of Gaussian white noise, it is observed in experiments that ECM is less efficient but more robust than ICP.

Chlorhexidine and preservation of sound tooth structure in older adults. A placebo-controlled trial

The Trial to Enhance Elderly Teeth Health (TEETH) was designed to test the impact of regular rinsing with a 0.12% chlorhexidine (CHX) solution on tooth loss, and the causes of tooth loss (caries, periodontal disease and trauma) were also investigated. This paper reports on the effectiveness of a 0.12% CHX solution for controlling caries using a tooth surface (coronal and root) survival analysis. A total of 1,101 low income elders in Seattle (United States) and Vancouver (Canada), aged 60-75 years, were recruited for a double-blind clinical trial and assigned to either a CHX (n = 550) or a placebo (n = 551) mouth rinse. Subjects alternated between daily rinsing for 1 month, followed by weekly rinsing for 5 months. All sound coronal and root surfaces at baseline were followed annually for up to 5 years. At each follow-up examination, those tooth surfaces with caries, restored, or extracted were scored as 'carious'. The hazard ratio associated with CHX for a sound surface to become filled, decayed, or extracted was 0.87 for coronal surfaces (95% confidence interval: 0.71-1.14, p = 0.20) and 0.91 for root surfaces (95% confidence interval: 0.73-1.14, p = 0.41). These findings suggest that regular rinsing with CHX does not have a substantial effect on the preservation of sound tooth structure in older adults.